PCA Projection and Missing Data
PCA Projection and Missing Data
Todo
- make better section headings
- add better descriptions
- profit
Load dataset and dependencies
Here we’re loading a genotype matrix with 159 individuals and ~30k SNPs. We selected three individuals from each Human Origins population.
# install dependencies
packages <- c("tidyverse", "cowplot", "softImpute", "missMethods", "norm", "mvtnorm", "ggrepel", "plotly", "magrittr")
Map(function(x) { install.packages(x) }, packages[!packages %in% utils::installed.packages()])## named list()library(magrittr)
library(ggplot2)
library(plotly)##
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## layoutknitr::opts_chunk$set(echo = FALSE, message = FALSE, warning = FALSE, fig.width = 8, fig.height = 6)
#setwd('~/Documents/exp_dat_reading_group_2021/session_4/')
source('helper_functions.R')
# load data
geno_matrix <- scan("geno_matrix_three.txt", what = "character") %>%
strsplit("") %>%
do.call(rbind, .) %>%
apply(., 2, as.numeric)
context_info <- readr::read_csv("context_info_three.csv")## Rows: 159 Columns: 6## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr (4): Individual_ID, Group_Name, Country, Makro_Region
## dbl (2): Longitude, Latitude##
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.###### Global PCAs
Here we plot two PCAs - the first uses all 159 individuals. For the second plot, we remove 9 individuals (plotted with red dots and labels), and then project those individuals onto a PCA generated from the remaining 150 individuals. You can see that there is some shrinkage towards the origin.
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Downsample data from projected individuals
First compute the static “background” PCA.
Projections with three levels of downsampling
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Now try many different downsample values, to see the effects of extremely large amounts of missing data.
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## 0.997projecting: 1 2 3 4 5 6 done.## Done.Plot this with an animation, where you can scan through the amount of data removed.
Try to get better boundaries
Here we do many iterations of the same amount of downsampling, to better observe the range of effects.
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## projecting: 1 2 3 4 5 6 7 8 9 done.## Done.non-African PCA
It’s possible that our dataset is not very susceptible to downsampling, due to the large amounts of variation present in the populations. Here we subset to just the non-African samples, and repeat some of the above experiments.
Basic non-African PCA (with and without projection)
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Try many levels of downsampling
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## 0.997projecting: 1 2 3 4 5 6 7 8 9 done.## Done.Debug PCA shrinkage
Ignore this
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